A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting
dc.contributor.author | Belmonte-Fernández, Óscar | |
dc.contributor.author | Sansano-Sansano, Emilio | |
dc.contributor.author | Caballer Miedes, Antonio | |
dc.contributor.author | Montoliu Colás, Raul | |
dc.contributor.author | García-Vidal, Rubén | |
dc.contributor.author | Gascó Compte, Arturo | |
dc.date.accessioned | 2021-04-14T07:52:51Z | |
dc.date.available | 2021-04-14T07:52:51Z | |
dc.date.issued | 2021-03-30 | |
dc.identifier.citation | Belmonte-Fernández, Óscar; Sansano-Sansano, Emilio; Caballer-Miedes, Antonio; Montoliu, Raúl; García-Vidal, Rubén; Gascó-Compte, Arturo. 2021. "A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting" Sensors 21, no. 7: 2392. https://doi.org/10.3390/s21072392 | ca_CA |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10234/192835 | |
dc.description.abstract | Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed. | ca_CA |
dc.format.extent | 25 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | MDPI | ca_CA |
dc.relation.isPartOf | Sensors 2021, 21(7), 2392; https://doi.org/10.3390/s21072392 | ca_CA |
dc.rights | Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/) | ca_CA |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | * |
dc.subject | hidden Markov models | ca_CA |
dc.subject | indoor localization | ca_CA |
dc.subject | machine learning | ca_CA |
dc.subject | Wi-Fi fingerprinting | ca_CA |
dc.title | A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.3390/s21072392 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.mdpi.com/journal/sensors | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Ministerio de Ciencia, Innovación y Universidades (Spain) | ca_CA |
project.funder.name | Universitat Jaume I | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
oaire.awardNumber | RTI2018-095168-B-C53 | ca_CA |
oaire.awardNumber | UJI-B2020-36 | ca_CA |
oaire.awardNumber | AICO/2020/046 | ca_CA |
oaire.awardNumber | PRX18/00123 | ca_CA |
Files in this item
This item appears in the following Collection(s)
-
IIDL_Articles [125]
-
INIT_Articles [752]
Except where otherwise noted, this item's license is described as Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/)